Machine learning forecasting based on residual predictions

Abstract

The present disclosure generally relates to systems, software, and computer-implemented methods for machine learning forecasting. One example method includes obtaining, using a first machine learning model, a first prediction predicting a variable for a first time period. A second prediction can be obtained, using a second machine learning model, to predict a residual of the first machine learning model for a second time period, where the first time period includes the second time period, and where the second machine learning model is trained based on at least a part of the first prediction. The first prediction and the second prediction can be combined to generate a combined prediction. One or more action recommendations can be generated based on the combined prediction.

What is claimed is: 
     
         1 . A system comprising:
 at least one memory storing instructions;   a network interface; and   at least one hardware processor interoperably coupled with the network interface and the at least one memory, wherein execution of the instructions by the at least one hardware processor causes performance of operations comprising:
 obtaining, using a first machine learning model, a first prediction predicting a variable for a first time period; 
 obtaining, using a second machine learning model, a second prediction predicting a residual of the first machine learning model for a second time period, wherein the first time period comprises the second time period, and wherein the second machine learning model is trained based on at least a part of the first prediction; 
 combining the first prediction and the second prediction to generate a combined prediction; and 
 generating one or more action recommendations based on the combined prediction. 
   
     
     
         2 . The system of  claim 1 , the operations comprising:
 obtaining one or more past residuals of the first machine learning model; and   training, using the one or more past residuals, the second machine learning model.   
     
     
         3 . The system of  claim 2 , wherein obtaining the one or more past residuals of the first machine learning model comprises:
 obtaining the at least a part of the first prediction, wherein the at least a part of the first prediction is associated with a third time period, and the first time period comprises the third time period;   obtaining an actual observation value of the variable for the third time period; and   subtracting the at least a part of the first prediction from the actual observation value to generate a past residual.   
     
     
         4 . The system of  claim 3 , wherein the first time period is relatively longer than the second time period. 
     
     
         5 . The system of  claim 1 , wherein combining the first prediction and the second prediction to generate the combined prediction comprises:
 adding the second prediction to the at least a part of first prediction.   
     
     
         6 . The system of  claim 1 , wherein generating the one or more action recommendations based on the combined prediction comprises:
 determining one or more Shapley values associated with the combined prediction; and   determining that the one or more Shapley values satisfy one or more conditions.   
     
     
         7 . The system of  claim 6 , the operations comprising:
 in response to determining that the one or more Shapley values satisfy the one or more conditions, adding one or more actions to the one or more action recommendations.   
     
     
         8 . The system of  claim 7 , wherein the one or more conditions comprise a condition that a sum of Shapley values associated with the first machine learning model are less than a predetermined ratio of a total sum of the Shapley values associated with the first machine learning model and Shapley values associated with the second machine learning model, and wherein the one or more actions comprise retraining the first machine learning model. 
     
     
         9 . The system of  claim 8 , the operations comprising:
 in response to determining that the sum of Shapley values associated with the first machine learning model are less than the predetermined ratio of the total sum of the Shapley values associated with the first machine learning model and the Shapley values associated with the second machine learning model, automatically triggering retraining of the first machine learning model.   
     
     
         10 . The system of  claim 1 , wherein the first machine learning model and the second machine learning model have at least one different feature. 
     
     
         11 . A computer-implemented method, comprising:
 obtaining, using a first machine learning model, a first prediction predicting a variable for a first time period;   obtaining, using a second machine learning model, a second prediction predicting a residual of the first machine learning model for a second time period, wherein the first time period comprises the second time period, and wherein the second machine learning model is trained based on at least a part of the first prediction;   combining the first prediction and the second prediction to generate a combined prediction; and   generating one or more action recommendations based on the combined prediction.   
     
     
         12 . The computer-implemented method of  claim 11 , comprising:
 obtaining one or more past residuals of the first machine learning model; and   training, using the one or more past residuals, the second machine learning model.   
     
     
         13 . The computer-implemented method of  claim 12 , wherein obtaining the one or more past residuals of the first machine learning model comprises:
 obtaining the at least a part of the first prediction, wherein the at least a part of the first prediction is associated with a third time period, and the first time period comprises the third time period;   obtaining an actual observation value of the variable for the third time period; and   subtracting the at least a part of the first prediction from the actual observation value to generate a past residual.   
     
     
         14 . The computer-implemented method of  claim 13 , wherein the first time period is relatively longer than the second time period. 
     
     
         15 . The computer-implemented method of  claim 14 , wherein combining the first prediction and the second prediction to generate the combined prediction comprises:
 adding the second prediction to the at least a part of first prediction.   
     
     
         16 . The computer-implemented method of  claim 15 , wherein generating the one or more action recommendations based on the combined prediction comprises:
 determining one or more Shapley values associated with the combined prediction; and   determining that the one or more Shapley values satisfy one or more conditions.   
     
     
         17 . The computer-implemented method of  claim 16 , comprising:
 in response to determining that the one or more Shapley values satisfy the one or more conditions, adding one or more actions to the one or more action recommendations.   
     
     
         18 . The computer-implemented method of  claim 17 , wherein the one or more conditions comprise a condition that a sum of Shapley values associated with the first machine learning model are less than a predetermined ratio of a total sum of the Shapley values associated with the first machine learning model and Shapley values associated with the second machine learning model, and wherein the one or more actions comprise retraining the first machine learning model. 
     
     
         19 . The computer-implemented method of  claim 18 , comprising:
 in response to determining that the sum of Shapley values associated with the first machine learning model are less than the predetermined ratio of the total sum of the Shapley values associated with the first machine learning model and the Shapley values associated with the second machine learning model, automatically triggering retraining of the first machine learning model.   
     
     
         20 . A non-transitory, computer-readable medium storing computer-readable instructions, that upon execution by at least one hardware processor, cause performance of operations, comprising:
 obtaining, using a first machine learning model, a first prediction predicting a variable for a first time period;   obtaining, using a second machine learning model, a second prediction predicting a residual of the first machine learning model for a second time period, wherein the first time period comprises the second time period, and wherein the second machine learning model is trained based on at least a part of the first prediction;   combining the first prediction and the second prediction to generate a combined prediction; and   generating one or more action recommendations based on the combined prediction.